problem_kicker
Agent sprawl creates unowned machine actors, unclear dependencies and capabilities that remain active after their original use case changes. Governance must cover runtime behavior and lifecycle, not only model selection.
DEMAND LANGUAGE / REAL-WORLD PROBLEM
“It works in the demo — but will it work in daily operations?”
“How do we measure whether the problem is actually solved?”
WHAT CAUSES THIS?
No authoritative inventory of agents and owners.
architecture_for AI AGENT GOVERNANCE
We establish an agent control plane model: identity, owner, purpose, dependencies, capabilities, data classes, approval policy, telemetry and retirement state become queryable records.
Ownership, least privilege, short-lived credentials and periodic entitlement review should be enforceable. High-risk actions need explicit approval and audit paths.
Governance telemetry should reveal execution volume, failure rates, cost and policy latency without turning the control plane into a single fragile bottleneck.
AI agents · agent registry · policy · observability · governance
failure_kicker
measure_kicker
verify_intro
CTO / CIO FAQ
At minimum identity, owner, purpose, runtime, dependencies, capabilities, data boundaries, policy, deployment/version and lifecycle state.
The interval should reflect risk and change rate; event-driven review is also important when ownership, dependencies or privileges change.
No. Model governance does not describe what an agent can execute, which systems it can mutate or who owns the resulting operational risk.
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